This study investigates the influence of process parameters on the mechanical properties and energy consumption of photopolymeric resins in the Masked Stereolithography (MSLA) 3D printing process. ELEGOO technology and resin were used to manufacture tensile specimens, according to ASTM D638-22 standards. The study employed a Full Factorial Design varying layer thickness (LT) and exposure time (ET) across three levels. Ultimate Tensile strength (UTS) and energy consumption were analyzed through experimental testing and nonlinear regression modelling. Results reveal that UTS decreases with increased LT, while higher ET enhances UTS. Energy consumption decreases as LT increases but rises with an increase of ET. Predictive models of UTS and Energy Consumption demonstrated high accuracy with R2 values of 0.99 for both UTS and energy consumption, indicating their robustness in prediction process dynamics. These findings provide critical insights for optimizing MSLA printing to balance mechanical performance and energy efficiency.

Effect of process parameters on mechanical properties of photopolymeric resin in masked stereolithography

Bianchi I.;Mancia T.;Simoncini M.;
2025-01-01

Abstract

This study investigates the influence of process parameters on the mechanical properties and energy consumption of photopolymeric resins in the Masked Stereolithography (MSLA) 3D printing process. ELEGOO technology and resin were used to manufacture tensile specimens, according to ASTM D638-22 standards. The study employed a Full Factorial Design varying layer thickness (LT) and exposure time (ET) across three levels. Ultimate Tensile strength (UTS) and energy consumption were analyzed through experimental testing and nonlinear regression modelling. Results reveal that UTS decreases with increased LT, while higher ET enhances UTS. Energy consumption decreases as LT increases but rises with an increase of ET. Predictive models of UTS and Energy Consumption demonstrated high accuracy with R2 values of 0.99 for both UTS and energy consumption, indicating their robustness in prediction process dynamics. These findings provide critical insights for optimizing MSLA printing to balance mechanical performance and energy efficiency.
2025
9781644903599
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11389/92595
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